Forecasting and Contracts Plan

Kibby and Strand require an innovative and effective production manager. This will be critical for Kibby and Strand as the manager will be responsible for forecasting a plan that will aim to maximize the output and minimize the input. The forecast plan below entails a clear set of objectives that are aligned with the overall objective of the organization. Each department has specific objectives the forecast plan aims to facilitate. In addition, the plan will also aim to assist

Human resource managers and marketing to conduct their responsibilities effectively and efficiently.

Forecast Objectives

The first objective of the forecast is to reduce the cost. This can be achieved by reducing and minimizing this can be done through reducing inventory that is in excess and avoiding stockouts. In addition, utilizing the available personnel, especially maximization of skills and experience. The second objective is to enhance the optimization of the available resources. The resources available will be utilized fully by enhancing production levels and also aligning the production levels with the demand for the products and services in the market $($K$$ck & Freitag $2021).$ In addition, the available resources will be efficiently utilized, thus avoiding underutilization or overutilization of the resources available. Another objective is to enhance sufficient and effective planning of the production. The main goal of this forecast plan is to ensure that, in the coming month, Kibby and Strand will meet the demand for their goods and services. Therefore, to meet this main objective, it$$s critical for Kibby and Strand to maximize output. The last objective is to ensure that Kibby and Strand adapt to changes quickly, both seasonality and trends. To equip the employees with adaptability skills, the forecast aims to incorporate patterns that determine patterns, trends, and seasonality.

Forecasting Data

There are mainly three data for forecasting outlined in this plan. The first is raw material data, which mainly assesses the availability of the raw materials to ensure there is a continuous production pattern. In addition, this data is very critical as it enhances the detection and resolution of issues that might arise in the supply chain process $($Fan et al$.,2021).$ The second data is historical production. This data uses the data of production from the past, which enhances the determination of the pattern arising from the analysis of the historical production, thus enhancing the identification of the trends and seasonality. Moreover, the market and demand data. This involves collecting data from the market, the factors without the control of the firm, and the behavior of the consumers, often referred to as consumer behavior which will predict the market demand. This will be achieved through teamwork with the sales and marketing departments.

Quantitative methods

The methods used will be quantitative, specifically time series analysis, technologically driven data methods, and collaborative forecasting. Time series analysis: This method will be used to identify the historical data, which will help inform the pattern. The method used is exponential smoothing and integrated moving average $($Zeroual et al$.,2020).$ Secondly, the technologically driven method will be a machine learning model that will be used for advanced forecasting using emerging technology. Lastly, the collaborative forecast will entail the merging of cross$-$functional teams relevant to achieving the set objectives. This team includes sales, marketing, and supply chain management, which are essential in providing relevant information.

Advantages and Disadvantages

There are various advantages posed by this forecast plan. One of the advantages is collaboration and teamwork facilitated by the integration of a cross$-$functional team, which will provide valuable insights. Another advantage is the accuracy provided by methods proposed by the forecast plan. On the other hand, there are various disadvantages posed by the forecast plan; one of these disadvantages is over$-$dependency on the data. Sometimes, there might be inconsistencies in the data collected, therefore leading to unreliable data and predictions. Another disadvantage is limitation$,$ especially on adaptability, especially when quantitative methods fail to predict future or unforeseen changes.

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